Did Twitter’s Removal of Government and State-Affiliated Media Labels Expand the Influence of State Actors?

Allison Koh

Digital Democracy Workshop
University of Zurich
October 27, 2023

#TBT

“Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda. We believe that people have the right to know when a media account is affiliated directly or indirectly with a state actor” (Twitter, 2020).

In the before-times, Twitter had a Trust and Safety team that had a very extensive system for increasing transparency for users and content moderation.

Adding labels flagging misinformation and potential political biases to tweets was part of their efforts to increase transparency on the platform, and their rationale for applying this to state actors was that “Unlike independent media, state-affiliated media frequently use their news coverage as a means to advance a political agenda”, as shown in this quote here.

Take the tweet shown here, for example: The headline reads “US warns China of ‘severe’ consequences for helping Russia”. The tweet is tagged with a label that indicates the account publishing it is “Iran state-affiliated media”. While some people might know without the label that Press TV is an Iranian state-affiliated media outlet, not everyone will be aware of this at face value. Without the label, it might be assumed that the US is taking more of a hawkish stance towards China, while the label tells us that this news is being published by an outlet that is funded by a government that is friendly to China and Russia.

Since Elon Musk’s takeover of the platform, Twitter has been undoing a lot of these efforts to add context to information circulated on the platform.

Twitter: A safe space for state influence?

State Actors on Twitter

Russian interference in the US 2016 election
(Golovchenko et al. 2020; Ross, Vaccari, and Chadwick 2022)

Chinese influence operations and Xinjiang
(DiResta et al. 2021)

Iranian involvement in anti-Saudi discourse
(Kießling et al. 2020; Romm 2018)

Effect of Twitter’s changes

State actor labels ⬇️ engagement
(Aguerri, Santisteban, and Miró-Llinares 2022; Liang, Zhu, and Li 2022)

Tweet engagement in far-right networks ⬆️ since October 2022
(Barrie 2022)

Prior research on state actors (from predominantly high-capacity authoritarian regimes) and Twitter’s changes have highlighted the wide range of contentious political activity across a variety of geopolitical contexts.

One of the most widely covered topics in the relevant research on state actors has been Russian interference in the US 2016 election. China has also used influence operations, often through companies backed by the Xinjiang Regional government, to skew the international discourse on their treatment of Uyghurs and Muslim minorities. The Iranian government has used Twitter to lodge smear campaigns against Saudi Arabia, with the objective of spreading misleading content about conflicts in the region.

Prior research finds that the introduction of profile labels since August 2020 has decreased engagement with Chinese and Russian state-affiliated media accounts. Since Elon Musk’s takeover of the platform in October last year, tweet engagement in far-right networks has also significantly increased.

Twitter’s Policy Changes in April 2023

**March 29:** ⬆️ engagement with prominent state media from China, Russia, Iran (Kann 2023)

**April 6:** ~~“In the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their Tweets with these labels to people.”~~

**April 12:** NPR quits Twitter after being labeled as “state-affiliated media” (Folkenflik 2023)

**April 21:** Twitter removes all labels for “government” and “state-affiliated media”

Many of Twitter’s policy changes concerning state actors, which ultimately led to the removal of relevant profile labels, rapidly escalated earlier this year.

On March 29th, The Atlantic Council’s Digital Forensics Lab identified increased engagement in terms of follows and likes, which signified a change in Twitter’s policy that stated “in the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their tweets with these labels to people”. Shown here: Average Twitter followers for Russia Today. On March 28th, when X-axes Jan 1 2023 -> sometime mid-April 2023

On April 6th, this clause was officially removed from Twitter’s Platform Use Guidelines.

Throughout April, Twitter started flagging outlets from the US, UK, and Canada as “state-affiliated” or “government-funded” media, going against their policy to not label public broadcasters that are editorially independent. This move essentially obscured the original intent for the labels to serve as a flag for politically-motivated content, and led NPR to quit the platform on April 12th.

After all this controversy over profile labels and whether public broadcasters from democracies with media freedom should be tagged with similar labels as state-affiliated media that follow the directive of their governments, Twitter removed all labels for “government” and “state-affiliated media” altogether.

Research Questions and Definitions

❓ Research Questions

After Twitter’s removal of profile labels on April 21st, 2023…

…did engagement with state actors increase?
…did state actors change how they use the platform?

📖 Definitions¹

State actor: Any individual or entity connected to government or state-affiliated media
Government official: Key individuals/entities representing “voices of the nation state abroad”
State-affiliated media: Outlets where the state exercises control over editorial content

Theoretical Expectations

Why should we expect changes on Twitter post-removal of state labels?

🧠 Key Concepts

Credibility¹: Perceived trustworthiness and expertise of an information source

Type	Attribute	Signals
Perceived	Source characteristics	profile labels, disclosed affiliations, account type, verification badges
Persuasive	Source behavior	post frequency, topic diversity, other content-based features

So why should we expect changes on Twitter after the removal of profile labels for state actors?

I argue that these platform changes offer opportunities for shifts in two different types of credibility.

Based on a defintion offered by communication scientists, I refer to credibility as the perceived trustworthiness and expertise of an information source.

In this research, I look at two different types of credibility. First, perceived credibility is a more passive form of credibility derived from characteristics of an information source. On social media, signals that help consumers of content infer perceived credibility include profile labels (the main focus of this research), disclosed affiliations, and account type. Verification badges are also a common signal for this type of credibility (as wonky as it is nowadays); but not relevant to this project.

On the flip side, persuasive credibility is a more active form of credibility derived from the behavior of the individual or entity producing content. In this study, I infer shifts in persuasive credibility by looking at two signals: post frequency and topic diversity.

Hypotheses and Exploratory Analysis

💡 Main Hypotheses

After Twitter officially removed labels from state actors’ accounts…

H1: …engagement with state actors increased.
- Perceived credibility: RTs/likes
H2: …state actors changed their behavior on the platform.
- Persuasive credibility: changes in post frequency/topic diversity
- Assumption: Lower topic diversity = more directed, pro-regime messaging¹

Hypotheses and Exploratory Analysis

🔎 Sub-hypotheses comparing state actor types

State actors with high credibility will be less responsive to platform changes.

🏦 Official government accounts are credible voices of the state regardless of profile label.
📺 State-affiliated media: more pronounced effects
- Labels signal limited editorial independence
- Label removal would boost perceived credibility
- Opportunities to manipulate information/boost persuasive credibility

Hypotheses and Exploratory Analysis

🌏 Exploratory Analysis

Variation between state actors from different countries

Data

State Actors: Hamilton 2.0 Dashboard¹

1,177 accounts linked to Chinese, Iranian, and Russian state actors

Proxy for accounts that had government and state-affiliated media labels

Data

Tweets: Twitter Academic API via `twarc` CLI

Tweet timeline endpoint; up to ~3,200 tweets per account

Collected ~2.5 million tweets from 1,038 timelines

Measurement

Dependent variables

Per-day metrics 📈

Retweets and likes of tweets authored by state actors (H1)
Tweet volumes of posts and Shannon Entropy of content produced by state actors (H2)

Independent variables

State actor type (official government or state-affiliated media)
Time-related variables for parametric tests

Results

Null results at the aggregate level

Placebo Tests—Engagement and Tweet Volume

Results

Disaggregated Analyses

Retweets

RTs of media accounts ⬆️

RTs of Russian accounts ⬆️

Results

Disaggregated Analyses

Likes

⬆️ likes of Russian accounts compared to Chinese/Iranian accounts

Results

Disaggregated Analyses

Tweets by state actors

⬆️ tweets by media accounts

⬆️ tweets by Russian and Chinese accounts

Conclusion

Results¹

Compared to official government accounts, volumes of engagement and post frequency significantly increased for state-affiliated media accounts.
Per-country differences²

Credibility

	China	Iran	Russia
Perceived	❌	❌	⬆️
Persuasive	⬆️	❌	⬆️

Next step: Measure per-day Shannon entropy, test hypotheses on topic diversity

Conclusion

Contributions

Descriptive, comparative overview of state actors’ online influence in the ✨ after times ✨
By removing profile labels for state actors, Twitter has increased the space available for some of these digital authoritarians to exert influence on the platform.
In this research area, it is important to account for digital authoritarians’ social media activity across actor types and geopolitical contexts.
Broader implications for understanding how changes in platform infrastructure can expand the reach of contentious political actors

Thank you!

koh@hertie-school.org

https://allisonkoh.github.io/

🟦 @allisonwkoh

Appendix

Twitter: A safe space for state influence?

Speaking of Elon Musk, after his acquisition of Twitter, the company became more vulnerable to interference from state actors. And when governments are able to have this much influence over content removal, it affects the free flow of information across borders.

To the left: We see that Twitter has been getting more court orders and government requests to remove content and hand over private data on individual users.

As shown on the right Between October 2022 and April 2023, Twitter fully complied with around 80% of legal demands from governments and courts. This was a definitive increase compared to how legal demands were handled in the past; in 2021, the compliance rate for such requests was approximately 50% (Twitter Transparency Center, 2022).

In addition, Twitter has also limited access to information on legal demands and other platform activity from researchers and third-party organizations. (and many of us in this room have very likely been affected by this)

References

Aguerri, Jesús, Mario Santisteban, and Fernando Miró-Llinares. 2022. “The Fight Against Disinformation and Its Consequences: Measuring the Impact of ‘Russia State-Affiliated Media’ on Twitter.” Preprint. SocArXiv. https://doi.org/10.31235/osf.io/b4qxt.

Appelman, Alyssa, and S. Shyam Sundar. 2016. “Measuring Message Credibility: Construction and Validation of an Exclusive Scale.” Journalism & Mass Communication Quarterly 93 (1): 59–79. https://doi.org/10.1177/1077699015606057.

Barrie, Christopher. 2022. “Did the Musk Takeover Boost Contentious Actors on Twitter?” arXiv Preprint arXiv:2212.10646. https://arxiv.org/abs/2212.10646.

DiResta, Renée, Josh A Goldstein, Carly Miller, and Harvey Wang. 2021. “One Topic, Two Networks: Evaluating Two Chinese Influence Operations on Twitter Related to Xinjiang.” Stanford Internet Observatory, December, 44.

Folkenflik, David. 2023. “NPR Quits Twitter After Being Falsely Labeled as ’State-Affiliated Media’.” NPR, April.

Golovchenko, Yevgeniy, Cody Buntain, Gregory Eady, Megan A. Brown, and Joshua A. Tucker. 2020. “Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube During the 2016 U.S. Presidential Election.” The International Journal of Press/Politics 25 (3): 357–89. https://doi.org/10.1177/1940161220912682.

Kann, Alyssa. 2023. “State-Controlled Media Experience Sudden Twitter Gains After Unannounced Platform Policy Change.” DFRLab.

Kießling, Bastian, Jan Homburg, Tanja Drozdzynski, and Steffen Burkhardt. 2020. “State Propaganda on Twitter.” In Disinformation in Open Online Media, edited by Christian Grimme, Mike Preuss, Frank W. Takes, and Annie Waldherr, 182–97. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-39627-5_14.

Liang, Fan, Qinfeng Zhu, and Gabriel Miao Li. 2022. “The Effects of Flagging Propaganda Sources on News Sharing: Quasi-Experimental Evidence from Twitter.” The International Journal of Press/Politics, March, 194016122210869. https://doi.org/10.1177/19401612221086905.

Munger, Kevin, Richard Bonneau, Jonathan Nagler, and Joshua A. Tucker. 2019. “Elites Tweet to Get Feet Off the Streets: Measuring Regime Social Media Strategies During Protest.” Political Science Research and Methods 7 (04): 815–34. https://doi.org/10.1017/psrm.2018.3.

Romm, Tony. 2018. “Iranians Masqueraded as Foreign Journalists to Push Political Messages Online, New Twitter Data Shows.” Washington Post, October.

Ross, Andrew RN, Cristian Vaccari, and Andrew Chadwick. 2022. “Russian Meddling in US Elections: How News of Disinformation’s Impact Can Affect Trust in Electoral Outcomes and Satisfaction with Democracy.” Mass Communication and Society 25 (6): 786–811.

Schafer, Bret. 2019. “Hamilton 2.0 Methodology & FAQs.” Alliance For Securing Democracy.

Twitter. 2020. “New Labels for Government and State-Affiliated Media Accounts.” https://blog.twitter.com/en_us/topics/product/2020/new-labels-for-government-and-state-affiliated-media-accounts.

Did Twitter’s Removal of Government and State-Affiliated Media Labels Expand the Influence of State Actors?

Allison Koh

#TBT

Twitter: A safe space for state influence?

State Actors on Twitter

Effect of Twitter’s changes

Twitter’s Policy Changes in April 2023

Research Questions and Definitions

❓ Research Questions

📖 Definitions1

Theoretical Expectations

Why should we expect changes on Twitter post-removal of state labels?

🧠 Key Concepts

Hypotheses and Exploratory Analysis

💡 Main Hypotheses

Hypotheses and Exploratory Analysis

🔎 Sub-hypotheses comparing state actor types

Hypotheses and Exploratory Analysis

🌏 Exploratory Analysis

Data

State Actors: Hamilton 2.0 Dashboard1

Data

Tweets: Twitter Academic API via twarc CLI

Measurement

Dependent variables

Independent variables

Results

Null results at the aggregate level

Results

Disaggregated Analyses

Results

Disaggregated Analyses

Results

Disaggregated Analyses

Conclusion

Results1

Conclusion

Contributions

Thank you!

Appendix

Twitter: A safe space for state influence?

References

📖 Definitions¹

State Actors: Hamilton 2.0 Dashboard¹

Tweets: Twitter Academic API via `twarc` CLI

Results¹